Prediction of Symptomatic Intracranial Hemorrhage Before Mechanical Thrombectomy Using Machine Learning in Patients with Anterior Circulation Large Vessel Occlusion
An ML model accurately predicted sICH prior to MT. It performed better than a standard statistical model and previously described clinical prediction models.
Authors
- Cheran Elangovan (ORCID: https://orcid.org/0000-0002-9473-8051)
- Daniel Hoit (ORCID: https://orcid.org/0000-0001-5380-7279)
- Nickalus R. Khan (ORCID: https://orcid.org/0000-0003-4099-089X)
- Lucas Elijovich (ORCID: https://orcid.org/0000-0003-1979-2848)
- Balaji Krishnaiah (ORCID: https://orcid.org/0000-0002-5825-1799)
- Christopher Nickele (ORCID: https://orcid.org/0000-0002-9412-4252)
- Joel Sequeiros Chirinos
- Nitin Goyal (ORCID: https://orcid.org/0000-0002-4699-0922)
- Adam S Arthur (ORCID: https://orcid.org/0000-0002-1536-1613)
- Haydn Hoffman (ORCID: https://orcid.org/0000-0002-2967-6528)
- Violiza Inoa (ORCID: https://orcid.org/0000-0003-0399-3346)
Institutions
- University of Tennessee Health Science Center (US)
- Semmes Murphey Foundation (US)
Publication Details
- Journal
- World Neurosurgery
- Published
- 2024-12-06
- DOI
- https://doi.org/10.1016/j.wneu.2024.11.038
- Citations
- 1
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.39